{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from sklearn import datasets\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x204194b1310>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 聚类前\n",
    "X = np.random.rand(100, 2)\n",
    "plt.scatter(X[:, 0], X[:, 1], marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化我们的质心，从原有的数据中选取K个座位质心\n",
    "def InitCentroids(X, k):\n",
    "    index = np.random.randint(0,len(X)-1,k)\n",
    "    return X[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#聚类后\n",
    "kmeans = KMeans(n_clusters=2).fit(X)\n",
    "label_pred = kmeans.labels_\n",
    "plt.scatter(X[:, 0], X[:, 1], c=label_pred)\n",
    "plt.show()"
   ]
  }
 ],
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